Neural networks can derive rules and patterns from real-world examples through training, making them suitable for learning from case data. Rule discovery is a process, not a specific technique. Signal processing deals with analog/digital signal manipulation, while case-based reasoning explicitly retrieves and adapts past cases rather than learning rules.